Explore the vast range of titles available.

The immune system is composed of a complex hierarchy of cell types that protect the organism against disease and maintain homeostasis. Identifying heterogeneity of immune cells is the key to understanding the immune system. Advanced single-cell RNA sequencing (scRNA-seq) technologies are revolutionizing our ability to study immunology. By measuring transcriptomes at the single-cell level, scRNA-seq enables identification of cellular heterogeneity in far greater detail than conventional methods. In this review, we introduce the existing scRNA-seq technologies and present their strengths and weaknesses. We also discuss potential applications and future innovations of scRNA-seq in immunology.

The rapid development of single-cell transcriptomic technologies has helped uncover the cellular heterogeneity within cell populations. However, bulk RNA-seq continues to be the main workhorse for quantifying gene expression levels due to technical simplicity and low cost. To most effectively extract information from bulk data given the new knowledge gained from single-cell methods, we have developed a novel algorithm to estimate the cell-type composition of bulk data from a single-cell RNA-seq-derived cell-type signature. Comparison with existing methods using various real RNA-seq data sets indicates that our new approach is more accurate and comprehensive than previous methods, especially for the estimation of rare cell types. More importantly, our method can detect cell-type composition changes in response to external perturbations, thereby providing a valuable, cost-effective method for dissecting the cell-type-specific effects of drug treatments or condition changes. As such, our method is applicable to a wide range of biological and clinical investigations. Bulk RNA-seq data harbors valuable information about gene expression levels from different cell types in tissue samples. Here, the authors develop DWLS, a computational method for estimating cell-type composition of bulk data by leveraging single-cell RNA-seq-derived cell-type signatures.

Single-cell analysis is a rapidly evolving approach to characterize genome-scale molecular information at the individual cell level. Development of single-cell technologies and computational methods has enabled systematic investigation of cellular heterogeneity in a wide range of tissues and cell populations, yielding fresh insights into the composition, dynamics, and regulatory mechanisms of cell states in development and disease. Despite substantial advances, significant challenges remain in the analysis, integration, and interpretation of single-cell omics data. Here, we discuss the state of the field and recent advances and look to future opportunities.

Single-cell analysis is a valuable tool for dissecting cellular heterogeneity in complex systems 1 . However, a comprehensive single-cell atlas has not been achieved for humans. Here we use single-cell mRNA sequencing to determine the cell-type composition of all major human organs and construct a scheme for the human cell landscape (HCL). We have uncovered a single-cell hierarchy for many tissues that have not been well characterized. We established a ‘single-cell HCL analysis’ pipeline that helps to define human cell identity. Finally, we performed a single-cell comparative analysis of landscapes from human and mouse to identify conserved genetic networks. We found that stem and progenitor cells exhibit strong transcriptomic stochasticity, whereas differentiated cells are more distinct. Our results provide a useful resource for the study of human biology. Single-cell RNA sequencing is used to generate a dataset covering all major human organs in both adult and fetal stages, enabling comparison with similar datasets for mouse tissues.

Stomach and intestinal stem cells are located in discrete niches called the isthmus and crypt, respectively. Recent studies have demonstrated a surprisingly conserved role for Wnt signaling in gastrointestinal development. Although intestinal stromal cells secrete Wnt ligands to promote stem cell renewal, the source of stomach Wnt ligands is still unclear. Here, by performing single cell analysis, we identify gastrointestinal stromal cell populations with transcriptome signatures that are conserved between the stomach and intestine. In close proximity to epithelial cells, these perictye-like cells highly express telocyte and pericyte markers as well as Wnt ligands, and they are enriched for Hh signaling. By analyzing mice activated for Hh signaling, we show a conserved mechanism of GLI2 activation of Wnt ligands. Moreover, genetic inhibition of Wnt secretion in perictye-like stromal cells or stromal cells more broadly demonstrates their essential roles in gastrointestinal regeneration and development, respectively, highlighting a redundancy in gastrointestinal stem cell niches. Wnt signals for intestinal stem cell self-renewal originate from the stroma and Paneth cells, but the source in stomach is unclear. Here the authors identify a conserved population of stromal cells adjacent to stomach epithelia where Gli2 activates Wnt ligands to promote gastrointestinal regeneration and development.

We present single-cell clustering using bifurcation analysis (SCUBA), a novel computational method for extracting lineage relationships from single-cell gene expression data and modeling the dynamic changes associated with cell differentiation. SCUBA draws techniques from nonlinear dynamics and stochastic differential equation theories, providing a systematic framework for modeling complex processes involving multilineage specifications. By applying SCUBA to analyze two complementary, publicly available datasets we successfully reconstructed the cellular hierarchy during early development of mouse embryos, modeled the dynamic changes in gene expression patterns, and predicted the effects of perturbing key transcriptional regulators on inducing lineage biases. The results were robust with respect to experimental platform differences between RT-PCR and RNA sequencing. We selectively tested our predictions in Nanog mutants and found good agreement between SCUBA predictions and the experimental data. We further extended the utility of SCUBA by developing a method to reconstruct missing temporal-order information from a typical single-cell dataset. Analysis of a hematopoietic dataset suggests that our method is effective for reconstructing gene expression dynamics during human B-cell development. In summary, SCUBA provides a useful single-cell data analysis tool that is well-suited for the investigation of developmental processes. Significance Characterization of cellular heterogeneity and hierarchy are important tasks in developmental biology and may help overcome drug resistance in treatment of cancer and other diseases. Single-cell technologies provide a powerful tool for detecting rare cell types and cell-fate transition events, whereas traditional gene expression profiling methods can be used only to measure the average behavior of a cell population. However, the lack of suitable computational methods for single-cell data analysis has become a bottleneck. Here we present a method with the focuses on automatically detecting multilineage transitions and on modeling the associated changes in gene expression patterns. We show that our method is generally applicable and that its applications provide biological insights into developmental processes.

Waddington’s epigenetic landscape is a metaphor frequently used to illustrate cell differentiation. Recent advances in single-cell genomics are altering our understanding of the Waddington landscape, yet the molecular mechanisms of cell-fate decisions remain poorly understood. We constructed a cell landscape of mouse lineage differentiation during development at the single-cell level and described both lineage-common and lineage-specific regulatory programs during cell-type maturation. We also found lineage-common regulatory programs that are broadly active during the development of invertebrates and vertebrates. In particular, we identified Xbp1 as an evolutionarily conserved regulator of cell-fate determinations across different species. We demonstrated that Xbp1 transcriptional regulation is important for the stabilization of the gene-regulatory networks for a wide range of mouse cell types. Our results offer genetic and molecular insights into cellular gene-regulatory programs and will serve as a basis for further advancing the understanding of cell-fate decisions. Single-cell RNA-sequencing of seven mouse developmental stages identifies lineage-specific and shared regulatory programs controlling cell-fate decisions. Cross-species analysis associates differentiation potency with ribosomal protein gene expression.

Background Human pluripotent stem cells (hPSCs) provide powerful models for studying cellular differentiations and unlimited sources of cells for regenerative medicine. However, a comprehensive single-cell level differentiation roadmap for hPSCs has not been achieved. Results We use high throughput single-cell RNA-sequencing (scRNA-seq), based on optimized microfluidic circuits, to profile early differentiation lineages in the human embryoid body system. We present a cellular-state landscape for hPSC early differentiation that covers multiple cellular lineages, including neural, muscle, endothelial, stromal, liver, and epithelial cells. Through pseudotime analysis, we construct the developmental trajectories of these progenitor cells and reveal the gene expression dynamics in the process of cell differentiation. We further reprogram primed H9 cells into naïve-like H9 cells to study the cellular-state transition process. We find that genes related to hemogenic endothelium development are enriched in naïve-like H9. Functionally, naïve-like H9 show higher potency for differentiation into hematopoietic lineages than primed cells. Conclusions Our single-cell analysis reveals the cellular-state landscape of hPSC early differentiation, offering new insights that can be harnessed for optimization of differentiation protocols.

The biology of haematopoietic stem cells (HSCs) has predominantly been studied under transplantation conditions 1 , 2 . It has been particularly challenging to study dynamic HSC behaviour, given that the visualization of HSCs in the native niche in live animals has not, to our knowledge, been achieved. Here we describe a dual genetic strategy in mice that restricts reporter labelling to a subset of the most quiescent long-term HSCs (LT-HSCs) and that is compatible with current intravital imaging approaches in the calvarial bone marrow 3 – 5 . We show that this subset of LT-HSCs resides close to both sinusoidal blood vessels and the endosteal surface. By contrast, multipotent progenitor cells (MPPs) show greater variation in distance from the endosteum and are more likely to be associated with transition zone vessels. LT-HSCs are not found in bone marrow niches with the deepest hypoxia and instead are found in hypoxic environments similar to those of MPPs. In vivo time-lapse imaging revealed that LT-HSCs at steady-state show limited motility. Activated LT-HSCs show heterogeneous responses, with some cells becoming highly motile and a fraction of HSCs expanding clonally within spatially restricted domains. These domains have defined characteristics, as HSC expansion is found almost exclusively in a subset of bone marrow cavities with bone-remodelling activity. By contrast, cavities with low bone-resorbing activity do not harbour expanding HSCs. These findings point to previously unknown heterogeneity within the bone marrow microenvironment, imposed by the stages of bone turnover. Our approach enables the direct visualization of HSC behaviours and dissection of heterogeneity in HSC niches. A dual genetic strategy enables the labelling and in vivo imaging of native long-term haematopoietic stem cells in the mouse calvarial bone marrow.

Formalin-fixed paraffin-embedded (FFPE) tissues constitute a vast and valuable patient material bank for clinical history and follow-up data. It is still challenging to achieve single cell/nucleus RNA (sc/snRNA) profile in FFPE tissues. Here, we develop a droplet-based snRNA sequencing technology (snRandom-seq) for FFPE tissues by capturing full-length total RNAs with random primers. snRandom-seq shows a minor doublet rate (0.3%), a much higher RNA coverage, and detects more non-coding RNAs and nascent RNAs, compared with state-of-art high-throughput scRNA-seq technologies. snRandom-seq detects a median of >3000 genes per nucleus and identifies 25 typical cell types. Moreover, we apply snRandom-seq on a clinical FFPE human liver cancer specimen and reveal an interesting subpopulation of nuclei with high proliferative activity. Our method provides a powerful snRNA-seq platform for clinical FFPE specimens and promises enormous applications in biomedical research. Formalin-fixed paraffin-embedded (FFPE) tissues constitute a vast and valuable patient material bank, but single nucleus RNAseq using such tissues is challenging. Here the authors develop a droplet-based method called snRandom-seq for high-throughput and sensitive single nucleus RNA-seq of FFPE samples.